The primary hypothesis in this work is that, due to the reality-gap, a policy repertoire evolved in the simulation undergoes a transformation on the outcome-space for the real robot. As a result, the safety and reward associated with those policies are also transformed. We capture the transformations of the safety and the reward functions with Gaussian process regression models [1]} using the simulated results as prior mean functions.
